The impact of airport competition on technical efficiency: A stochastic frontier analysis applied to Italian airport

Scotti, Davide; Malighetti, Paolo; Martini, Gianmaria; Volta, Nicola

doi:10.1016/j.jairtraman.2012.01.003

Cited by 81 publications

(28 citation statements)

References 13 publications

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“…This dataset provides information about estimated population density for the EU27, Croatia and Moldova at a pixel size of one hectare. This is a level of detail much higher than the NUTS 3 level used in previous analyses (e.g., Redondi et al, 2013;Scotti et al, 2012;Lieshout, 2012;Papatheodorou and Arvanitis, 2009;Grosche et al, 2007). Table 1 shows the substantial improvement in terms of data disaggregation that CORINE represents over the NUTS units.…”

Section: Datamentioning

confidence: 87%

“…European studies considering population in the catchment area usually take the NUTS 3 level 1 to aggregate population values around the airport (e.g., Papatheodorou and Arvanitis, 2009;Grosche et al, 2007). Two recent studies use lower levels of data aggregation than NUTS 3, Redondi et al (2013) use municipality level units and Scotti et al (2012) use zip codes, both represent an advance. Nevertheless, when aggregating point-based geospatial values e such as population e into pre-defined districts, results are influenced by the choice of the district boundaries, which becomes a source of statistical bias.…”

Section: Introduction: the Modifiable Area Unit Problemmentioning

confidence: 99%

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An appraisal of the CORINE land cover database in airport catchment area analysis using a GIS approach

Suau-Sánchez

Burghouwt²,

Pallarès-Barberà

2014

Journal of Air Transport Management

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a b s t r a c tThis paper presents a free available dataset, the CORINE land cover that helps dealing with the biases caused by pre-defined and heterogeneous census district boundaries in airport catchment area analysis in Europe. Using this dataset and a conventional GIS software it is possible to measure the size of the population within catchment areas at the same spatial level for all EU airports, allowing for consistent comparisons among airports. To illustrate the potential of the CORINE/GIS approach, the size of the population in the catchment areas of all European airports was determined. The empirical exercise has an aggregate perspective, but this database presents many other possibilities of analysis to perform in a case-by-case basis.

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Section: Datamentioning

confidence: 87%

Section: Introduction: the Modifiable Area Unit Problemmentioning

confidence: 99%

An appraisal of the CORINE land cover database in airport catchment area analysis using a GIS approach

Suau-Sánchez

Burghouwt²,

Pallarès-Barberà

2014

Journal of Air Transport Management

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“…see Gillen and Lall, 1997) and Stochastic Frontier Analysis (SFA) (e.g. see Scotti et al, 2012). Both techniques are valid and have been fruitfully applied in the context of transport geography (see Grubesic and Wei, 2012;Martin and Roman, 2001;and Tapiador et al, 2008 for examples of DEA application); yet, they do not consider risk at least explicitly.…”

Section: Discussionmentioning

confidence: 99%

Air transport liberalisation and airport dependency: developing a composite index

Koo

Halpern

Παπαθεοδώρου

et al. 2016

Journal of Transport Geography

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Air transport liberalisation in Europe has produced some major changes to the networks operated by airlines and the services available at airports. Within this context the degree of airport dependency in terms of market, spatial and temporal concentration is important to know from an economic geography and risk management perspective. A composite index called the Airport Dependency Index (ADI) is developed to measure airport dependency based on the concept of the relative Gini coefficient. Liberalisation has had varying impacts depending on the size and type of airport and so a comparison is made of the degree of dependency at a large sample of European airports using the ADI. The ADI has the potential to provide insight on the sustainability and worthiness of financing airport projects, and on whether airports should diversify further their activities by investing in the growth and expansion of their network.Keywords: Air transport liberalisation; Airport Dependency Index (ADI); Gini coefficient; European airports. Highlights A composite Gini-based index is developed to measure airport dependency.  Dependency is measured according to market, spatial and temporal concentration.  Data is collected and analysed over a ten year period from 2005 to 2014.  735 European airports are compared.  Implications for impact/added value analysis are considered.2

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“…The first refers to parametric methods, such as stochastic frontier analysis (SFA), which measure efficiency through econometric techniques [10,[28][29][30][31][32][33]. The second comprises investigations applying the non-parametric methodology called DEA [13,[34][35][36][37].…”

Section: Dea Studies In the Air Transportmentioning

confidence: 99%

“…The deregulation process in Europe has followed four steps [8][9][10], which have led to a unique domestic market throughout the countries. The last step has taken place in 2008 when, due to the Regulation (EC) No.…”

Section: Introductionmentioning

confidence: 99%

Measuring and Explaining Airport Efficiency and Sustainability: Evidence from Italy

2018

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Abstract:From an environmental point of view, it is widely recognized in economic literature that an efficient management of regional airports produces positive effects both for congestion reduction in the larger airports and for better use of existing infrastructures. Regional airports generally suffer from economic vulnerabilities because of scarcity of traffic volume; besides, their small catchment areas often determine low cash flow levels. As a result, significant problems of economic sustainability arise. In this context, airport infrastructure providers have the incentive to hide failures in their strategic decisions, justifying the low share of cost recovery through market characteristics. By means of the DEA (Data Envelopment Analysis) method this paper analyses overall technical, pure technical, and scale efficiency of 34 Italian airports in the period 2006-2016 in order to investigate how a number of factors impact on the efficiency and economic sustainability of regional airports. Our findings reveal that airport size, presence of low-cost carriers and cargo traffic have a significant influence on the technical and scale efficiency of Italian airports. In other words, air transport privatization and deregulation can positively affect regional airport efficiency and sustainability. This is to say that the market mechanism is a useful tool in achieving regional airport sustainability even if the empirical analysis of the effects of privatization and deregulation is recommended for evaluating such political programs.

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The impact of airport competition on technical efficiency: A stochastic frontier analysis applied to Italian airport

Cited by 81 publications

References 13 publications

An appraisal of the CORINE land cover database in airport catchment area analysis using a GIS approach

An appraisal of the CORINE land cover database in airport catchment area analysis using a GIS approach

Air transport liberalisation and airport dependency: developing a composite index

Measuring and Explaining Airport Efficiency and Sustainability: Evidence from Italy

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